Search results

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

This paper aims to solve the problem of uncertain position and attitude between unstructured terrain robot and grasped target and insufficient control accuracy in extreme environment, a grasping mechanism based on attraction domain relationship is proposed, which can realize autonomous positioning, capturing and grasping of robot under low control accuracy.

The grasping mechanism was designed, taking inspiration from fishing behavior this mechanism introduces attraction domains and flexible-elastic structures through the active and passive ends to achieve automatic positioning and capture. After the capture is completed, the grasping mechanism connects the active end and the passive end, simultaneously relying on the gravity of the target object to achieve locking and release between the robot and the target object. This paper adopts theoretical, simulation and experimental verification methods to conduct theoretical and simulation analysis on the autonomous positioning and grasping process of the mechanism, and produces grasping experimental prototypes with different positions and postures.

The experiment shows that the gripping mechanism designed in this paper can achieve automatic positioning capture and gripping of large deviation situations under low control accuracy, with a displacement deviation of up to 10 mm (about 1/6 diameter of the end of the mechanism) and an angle deviation of up to 3°. The scientific research task in the extremely high altitude environment has finally been successfully accomplished.

Inspired by fishing behavior, this paper proposes a positioning, capturing and grasping mechanism. The attraction area built with permanent magnets, coupled with the flexible connection, enables precise capture under low control, while the grasping mechanism can also rely on gravity to self-lock and release.

The relationship between corporate-level green entrepreneurial orientation (GEO) and the implementation of cleaner production practices (CPPs) is to a great extent unexplored in previous literature. This study aims to connect them with the corporate-level green supply chain learning and organizational competitive aggressiveness, presenting a novel interpretation of social ethics and morality in CPPs.

To thoroughly explore this novel relationship, this study has used PLS-SEM for examining the moderated-mediation of competitive aggressiveness and green supply chain learning.

Findings based on the data collected from 176 German firms have largely confirmed our propositions. Suggesting that there is a strong positive relationship between corporate level GEO and CPPs. Furthermore, our findings have confirmed that green supply chain learning mediates the macro-level relationship between GEO and CPPs. Moreover, it is also confirmed that the mediation of green supply chain learning in the macro-level relationship between GEO and CPPs is negatively moderated by competitive aggressiveness.

This study offers a first-hand view of negatively moderated-mediation of competitive aggressiveness. Therefore, its findings are extremely relevant for policymakers in the domain of morality, social ethics, corporate-level GEO, sustainability, corporate-level green supply chain learning and CPPs.

This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.

Laser surface re-melting (LSR) was used to manufacture various textured patterns (i.e. line, grid and mixed) on both the original and heat-treated 70Mn steel plates. The micro-hardness, microstructure, tensile strength, yield strength, elongation, coefficients of friction (COF) and worn morphologies were characterized to evaluate the impact of different textured patterns on the overall performance.

The results show that re-melted unit exhibited the highest surface hardness on the subsurface. The increase in surface hardness of the re-melted unit for the heat-treated 70Mn steel samples was much lower than that of the original ones. The re-melted textured patterns did not improve the tensile strength, yield strength and elongation of either original or heat-treated 70Mn steel samples. The re-melted textured patterns effectively reduced the average COFs of heat-treated 70Mn steel samples, but increased friction of the non-heat-treated samples.

This study provides valuable insights into enhancing the mechanical properties and tribological characteristics of 70Mn steel, particularly in the automotive, heavy machinery and high-load application sectors. These industries have stringent requirements for durability and friction control, and the findings of this research are expected to effectively extend the lifespan of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0443/

Blockchain technology (BCT) has emerged as a powerful tool for enhancing transparency and trust. However, the relationship between the benefits of BCT and agri-food supply chain performance (AFSCperf) remains underexplored. Therefore, the current study investigates the influence of BCT on AFSCperf and sustainability issues.

Through a comprehensive literature review, various benefits of BCT are identified. Subsequently, a research framework is proposed based on data collected from questionnaire surveys and personal visits to professionals in the agri-food industry. The proposed framework is validated using partial least square structural equation modelling (PLS-SEM).

The findings reveal that BCT positively impacts AFSCperf by improving traceability, transparency, food safety and quality, immutability and trust. Additionally, BCT adoption enhances stakeholder collaboration, provides a decentralised network, improves data accessibility and yields a better return on investment, resulting in the overall improvement in AFSCperf and socio-economic sustainability.

This study offers valuable practical insights for practitioners and academicians, establishing empirical links between the benefits of BCT and AFSCperf and providing a deeper understanding of BCT adoption.

Stakeholders, managers, policymakers and technology providers can leverage these findings to optimise the benefits of BCT in enhancing AFSCperf. Moreover, it utilises rigorous theoretical and empirical approaches, drawing on a multidisciplinary perspective encompassing food operations and supply chain literature, public policy, information technology, strategy, organisational theory and sustainability.

The current study aims to investigate the impact of perceived blockchain-related information transparency on consumers’ intention to purchase organic food. This study examines how perceived blockchain- related information transparency, directly and indirectly, affects purchase intentions through attitudes, perceived behavioural control and subjective norms. Additionally, the study explores how blockchain-based trust moderates the influence of perceived blockchain-related information transparency on these factors and the intention to purchase organic food.

Based on the theory of planned behaviour framework and a sample of 5,326 consumers, this study uses partial least squares structural equation modelling to test the research model.

This study finds that perceived blockchain-related information transparency directly enhances consumers’ attitudes towards organic food purchase, perceived behavioural control, subjective norms and intention to purchase organic food. Additionally, perceived blockchain-related information transparency indirectly affects consumers’ intention to buy organic food through three antecedents of the theory of planned behaviour model. Notably, these indirect effects were moderated by consumers’ blockchain-based trust.

This study provides recommendations for leveraging blockchain to enhance transparency and build trust, which could boost consumer engagement and organic food purchases.

This research contributes to blockchain literature by empirically examining the role of perceived blockchain-related transparency and blockchain-based trust in consumers’ purchasing decisions regarding organic food. It provides valuable insights into the consumer-centric benefits of blockchain technology. Furthermore, this study also contributes to the literature on organic food, particularly its promotion through blockchain technology.

The rise of the mobile Internet has accumulated much text information in various online financial forums. Such information often contains the emotional attitudes of investors toward financial technology (fintech) platforms, so extracting the sentimental tendency information has great practical value for the development of fintech platforms. Based on the investor sentiment theory, the paper aims to analyze the relevant social media data and test the influence path of online news evaluation on the stock price fluctuation of fintech platforms.

Taking Oriental Fortune as the research object, this paper selects multiple variables such as stock bar popularity, snowball popularity, news popularity and news sentiment scores collected by UQER and combines the sentiment scores of single daily news into a daily sentiment score. Based on the period from November 1, 2019 to March 31, 2020, during the emergence of the coronavirus disease 2019 (COVID-19) pandemic as the background, the authors conduct the Granger causality test based on the vector autoregressive (VAR) model and analyze the relevant evaluation of Oriental Fortune through the empirical model.

The authors' results show that different online evaluations impact the rise and fall of stock prices differently, while news popularity has the most significant impact. Besides, news sentiment scores on share price fluctuation have a relatively substantial influence. These findings indicate that the authoritative news evaluation can strongly guide investors to make relevant investment behavior operations in the information dissemination process, significantly affecting stock prices.

The research findings of this paper have good inspiration and reference values for investors and financial regulators.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.